---
license: apache-2.0
base_model:
- Qwen/Qwen2.5-7B-instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- uncensored
- code
- legal
- text-generation-inference
---

# Gabliterated Model Series

![Logo/JPG](Gabliteration-logo.jpg)

## Overview

With this model series, I introduce the first **Gabliteration**, a novel neural weight modification technique that advances beyond traditional abliteration methods through adaptive multi-directional projections with regularized layer selection.
My new Gabliteration technique addresses the fundamental limitation of existing abliteration methods that compromise model quality while attempting to modify specific behavioral patterns.

## Model Variants

This series includes models ranging from 0.6B to 32B parameters, demonstrating the scalability and effectiveness of the Gabliteration technique across different model sizes.

## Quants

- [GGUF (mradermacher)](https://huggingface.co/mradermacher/)
- [i1 GGUF (mradermacher)](https://huggingface.co/mradermacher/)

## Technical Background

Building upon the foundational work of Arditi et al. (2024) on single-direction abliteration, Gabliteration extends to a comprehensive multi-directional framework with theoretical guarantees.
My method employs singular value decomposition on difference matrices between harmful and harmless prompt representations to extract multiple refusal directions.

## Citation

If you use these models, please cite the original research (paper comming later this year):

```
Gülmez, G. (2025). Gabliteration: Adaptive Multi-Directional Neural Weight Modification for Selective Behavioral Alteration in Large Language Models.
```

## Acknowledgments

This work builds upon the foundational research by Arditi et al. (2024) on refusal direction identification in large language models.